【报告摘要】Change-point detection is a classical topic in statistics with numerous applications. Motivated by the presence of structural breaks in complex data types such as networks, distributions, and covariance matrices, there has been a surge of interest in detecting change points within non-Euclidean-valued time series. In this presentation, we will introduce a new nonparametric approach for testing and estimating change points in non-Euclidean time series data using self-normalization. The discussion will begin with a review of self-normalization's role in time series inference, focusing on its application in constructing confidence intervals and testing for changes in the mean. This talk will also provide an overview of my journey using self-normalization for various inference problems in time series analysis, with a particular emphasis on change-point detection.

 

【报告人简介】Xiaofeng Shao is a professor in the Department of Statistics and Data Science, as well as the Department of Economics, at Washington University in St. Louis. He earned his PhD in Statistics from the University of Chicago in 2006 and has been a faculty member in the Department of Statistics at the University of Illinois Urbana-Champaign for 18 years. His current research interests include time series analysis, change-point analysis, functional data analysis, high-dimensional data analysis, and their applications. He is a fellow of the Institute of Mathematical Statistics (IMS) and the American Statistical Association (ASA). Currently, he serves as an associate editor for the Annals of Statistics, Journal of the Royal Statistical Society: Series B, Journal of the American Statistical Association, and Journal of Time Series Analysis.